Multidisciplinary Design and Collaborative Optimization for Excavator Backhoe Device

Abstract

The excavator working device is a typical mechanical system of electromechanical liquid that is complex. Traditional optimization design methods are difficult to get global optimized results of excavator backhoe device through the serial mode of “mechanism-load-structure”. Thus, the theory of parallel collaborative optimization (CO) is applied. To establish a sophisticated CO model of the backhoe device, a certain excavator is investigated as a sample multidisciplinary CO (MDCO) design. To generate the CO model, an improved optimization algorithm called the particle swarm-genetic algorithm （PS-GA）is proposed. To verify the MDCO design of the excavator backhoe device, a parameterized virtual prototype (VP) of the backhoe device is established in ADAMS. This VP is optimized by applying the MDCO design results to the parameterized VP. The VP of the backhoe device is also optimized by a single discipline when the optimization results from a single discipline are inputted into the parameterized VP. Both optimized VPs are simulated under similar conditions, and results show that in the MDCO design, the arm crowd force of the backhoe device is 8.1% stronger than that in the design optimized by a single discipline under constant power and oil pressure conditions. Similarly, breakout force increased by approximately 8.3%. The quality (volume) of the entire backhoe device decreased by 9.5%; however, the maximum stress of each characteristic partition changed only slightly. Therefore, the MDCO design effectively and practically addresses problems regarding the optimization of the design of complex mechanical systems.